Big data analysis of network metadata for optical networking systems

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Optigo Networks is shaping the future of the commercial Internet of Things (IoT) by redefining how smart buildings are connected. By addressing cybersecurity threats in the building network, Optigo allows the IoT to expand securely, enabling the technologies that make buildings energy efficient, comfortable and secure. Optigo Networks provides a complete solution to protect and connect today's most advanced buildings. Layered on top of a robust networking platform, Optigo's integrated big data analytics and security engine ensures that building systems are protected and operate trouble-free. This research project will help advance Optigo Network's next-gen analytics engine, which uses network metadata to identify anomalies within a building network. Using parameters such as traffic rates, source and destination addressing, peer communications and power consumption. This project aims to use machine learning and other statistical and big data tools to detect or even predict potential problems in a real-time network. Potential applications include identifying security anomalies and failures in devices. I wish to determine whether big data techniques common in theoretical and experimental physics can be used to identify anomalies in the data collected by Optigo Networks' product line. Using our expertise in data processing and modelling, we hope to identify patterns and profiles in the network metadata that Optigo Networks can use to advance their anomaly detection tools.